An adaptive neural network approach for automatic modulation recognition

Ettefagh, Yasaman; Moghaddam, Mohammad Hossein; Eghbalian, Sajjad

doi:10.1109/ciss.2017.7926153

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…The modulation recognition signals include BASK, QASK, BFSK, QFSK, BPSK, QPSK, OQAM, and MSK. We use the MSNR to describe the signal and noise power ratio and given by equation (8), and the simulation parameter of modulation signals and alpha-stable distribution model is presented in Table 1. The simulation parameters of the QEHA-BPNN is presented in Table 2.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The current commonly used modulation signal feature extraction method includes instantaneous amplitude, frequency ,and phase [3], high-order cumulants [4], wavelet-based features [5], and so on. And then, the decision tree (DR) [6], support vector machine (SVM) [7], neural network [8], and other classifiers are trained according to the extracted feature parameters to realize the recognition of modulation signals.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

2021

KSII TIIS

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In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

2021

KSII TIIS

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“…Generally, the relevant previous researches are categorized into likelihood-based (LB) and feature-based (FB) [2]. The former one provides an optimal performance with extremely high computational complexity, while the latter one can achieve a suboptimal performance with low computational complexity [9]. The FB method contains two steps which are feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

Zhou

2023

Chinese J. Elect.

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The feature-based (FB) algorithms are widely used in modulation classification due to their low complexity. As a prerequisite step of FB, feature selection can reduce the computational complexity without significant performance loss. In this paper, according to the linear separability of cumulant features, the hyperplane of the support vector machine is used to classify modulation types, and the contribution of different features is ranked through the weight vector. Then, cumulant features are selected using recursive feature elimination (RFE) to identify the modulation type employed at the transmitter. We compare the performance of the proposed algorithm with existing feature selection algorithms and analyze the complexity of all the mentioned algorithms. Simulation results verify that the proposed RFE algorithm can optimize the selection of the features to realize modulation recognition and improve identification efficiency.

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“…Wireless communication architectures are gradually becoming intelligent and automated. Among them, some researchers proposed intelligent solutions for automatic modulation classification (AMC), which promotes wireless signal recognition, spectrum monitoring [1,2]. In wireless signal application scenarios, cognitive radio (CR) mainly addresses spectrum allocation and management, and AMC is a critical task to be solved urgently [3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning for Automatic Modulation Classification

Nie¹,

Tan²,

Zhang³

2021

Preprint

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Automatic modulation classification (AMC) plays an increasingly vital role in cognitive radio (CR), cognitive electronic warfare, and other areas. It aims at classifying the modulated modes of the received signals accurately and provides a guarantee for the subsequent detailed parameter identification. Deep learning (DL) methods allow the computer to automatically learn the pattern features and integrate features into the process of building the model, thereby reducing the incompleteness caused by artificial design features. At the same time, the DL methods have been applied in the AMC field as its powerful ability to process complex data and have achieved excellent performance in recent years. In this paper, we propose a deep ensemble learning AMC network, which uses a multi-model ensemble method to fuse multiple DL features. Specifically, different DL models are integrated by ensemble learning, which enhances the learning ability of the single model. With the proposed ensemble model trained on a measured wireless signal dataset, we conclude that the ensemble structure of Inception and CLDNN can fuse spatial features and temporal features, and achieve state-of-the-art performance in AMC tasks. Besides, the impact of the inphase/quadrature (I/Q) sample-length on wireless signals is further investigated, and find that the classification accuracy of the deep ensemble model is improved by 0.7% to 10% compared to the single model under various sample-length. Simultaneously, we visualize convergence clustering with t-distributed stochastic neighbor embedding (t-SNE), and the visualization results prove that the deep ensemble model has a stronger clustering ability than a single model.

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An adaptive neural network approach for automatic modulation recognition

Cited by 12 publications

References 4 publications

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

Recursive Feature Elimination Based Feature Selection in Modulation Classification for MIMO Systems

Deep Ensemble Learning for Automatic Modulation Classification

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